# CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps

arXiv: https://arxiv.org/abs/2004.13795 · HTML: https://mlsanity.com/paper/2004.13795/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM](https://mlsanity.com/paper/2103.05056/) (2021)
- [CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration](https://mlsanity.com/paper/2402.00129/) (2024)
- [Fairness and Bias in Robot Learning](https://mlsanity.com/paper/2207.03444/) (2022)
- [End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization](https://mlsanity.com/paper/2306.11346/) (2023)
- [Amodal Optical Flow](https://mlsanity.com/paper/2311.07761/) (2023)
- [RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps](https://mlsanity.com/paper/2309.09875/) (2023)
- [Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation](https://mlsanity.com/paper/2205.08316/) (2022)
- [Poses as Queries: Image-to-LiDAR Map Localization with Transformers](https://mlsanity.com/paper/2305.04298/) (2023)
- [What Really Matters for Learning-based LiDAR-Camera Calibration](https://mlsanity.com/paper/2501.16969/) (2025)
- [EVLoc: Event-based Visual Localization in LiDAR Maps via Event-Depth Registration](https://mlsanity.com/paper/2503.00167/) (2025)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)
- [AnchorD: Metric Grounding of Monocular Depth Using Factor Graphs](https://mlsanity.com/paper/2605.02667/) (2026)
- [CRISTAL: Real-time Camera Registration in Static LiDAR Scans using Neural Rendering](https://mlsanity.com/paper/2511.16349/) (2025)
- [Joint Target-Less Intrinsic and Extrinsic Camera-LiDAR Calibration using Deep Point Correspondences](https://mlsanity.com/paper/2605.23397/) (2026)

## Abstract (arXiv)

Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to make a sizeable impact on improving capabilities of metric visual localization. One of the major hindrances has been the inability of existing Convolutional Neural Network (CNN)-based pose regression methods to generalize to previously unseen places. Our recently introduced CMRNet effectively addresses this limitation by enabling map independent monocular localization in LiDAR-maps. In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters. We enable this capability by combining deep learning with geometric techniques, and by moving the metric reasoning outside the learning process. In this way, the weights of the network are not tied to a specific camera. Extensive evaluations of CMRNet++ on three challenging autonomous driving datasets, i.e., KITTI, Argoverse, and Lyft5, show that CMRNet++ outperforms CMRNet as well as other baselines by a large margin. More importantly, for the first-time, we demonstrate the ability of a deep learning approach to accurately localize without any retraining or fine-tuning in a completely new environment and independent of the camera parameters.